As organizations rapidly adopt artificial intelligence agents, cybersecurity experts are emphasizing that effective protection begins with visibility into how these systems operate. The discussion around AI agents has increasingly shifted from deployment speed and productivity benefits toward understanding security risks, access controls and governance requirements. Recent AI related security incidents have encouraged organizations to evaluate whether their ability to monitor and secure autonomous workflows is keeping pace with adoption. Security teams are increasingly asking what resources an AI agent can access once deployed, how those permissions are managed and whether suspicious activity can be identified before it creates business impact.
Research shared by Veeam indicates that many organizations are already allowing AI workflows to interact with sensitive business data without complete oversight. The findings highlight a growing challenge around visibility, particularly as employees create AI powered workflows outside traditional technology management processes. Shadow AI has emerged as a major concern because organizations may struggle to identify every AI tool, agent or automated workflow operating within their environment. Security professionals emphasize that Zero Trust principles can support AI governance programs, but only when implemented in the correct sequence. Before organizations introduce enforcement policies, authorization layers or monitoring controls, they must first understand what AI agents exist, who owns them, what data they can access and how they are being used. One major challenge is that AI agent adoption is creating a new form of shadow technology use. Similar to earlier challenges with cloud services and unauthorized applications, employees often adopt new tools faster than governance frameworks can be established. Security teams may consider blocking unknown AI tools, but restricting access without understanding existing usage can also affect legitimate business activities. Experts suggest organizations should first create visibility by monitoring AI related spending, API key usage, approved service providers and technology procurement activity. Establishing an approved path for AI adoption can help organizations identify legitimate use cases while reducing unmanaged deployments. The principle of understanding AI usage before applying restrictions is becoming an important part of modern security planning.
Another challenge involves the difficulty of gaining complete visibility from a single security source. AI agents can operate across networks, endpoints, browsers, SaaS platforms and cloud environments, creating multiple areas where traditional monitoring methods may not provide a complete picture. Network monitoring may identify communication with AI providers but may not reveal the exact prompts, actions or data interactions involved. Endpoint tools may miss browser based AI applications, while SaaS integrated AI features can remain outside traditional security visibility. Security teams are therefore encouraged to combine multiple sources of information, including network metadata, endpoint activity, browser telemetry, identity records, API key activity and cloud logs, to develop a broader understanding of AI agent usage. Continuous monitoring is also becoming increasingly important as AI agents can be created, modified or duplicated much faster than traditional technology assets. Periodic audits may not provide an accurate view of rapidly changing AI environments, creating gaps that attackers could potentially exploit. Security professionals recommend combining automated monitoring with human accountability, ensuring every AI agent has a defined identity, responsible owner and controlled permissions. Logging should also expand beyond user interactions to include the actions performed by AI agents, such as tool usage, connected services and data movement. While concepts such as emergency shutdown mechanisms for autonomous AI systems are receiving attention, experts note that such controls are only effective when organizations already know which systems need to be controlled.
The broader approach to Zero Trust for AI agents begins with discovery, governance and continuous visibility before enforcement measures are introduced. Organizations are encouraged to build inventories of AI systems, establish clear ownership, monitor agent activity and apply security policies based on identified risks. As AI becomes increasingly integrated into enterprise operations, security strategies will need to evolve from simply controlling access toward understanding and managing autonomous systems throughout their lifecycle. Visibility remains a foundational requirement because organizations cannot effectively secure, monitor or govern AI agents that they cannot identify.
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